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# Library
library(dplyr)
library(ggplot2)
# load data
## Isotope data
isotope_data <- utils::read.csv(here::here("data", "isotopic_data_2021.csv"), sep = ";", header = T,dec = ",")
isotope_data_fish <- isotope_data %>%
# only fish
filter(taxon == "Fish")%>%
mutate(species=recode(species, "Cyclothone"="Cyclothone spp."))%>%
arrange(species)
## trawling data
trawling_data_evhoe21 <- utils::read.csv(here::here("data", "trawling_data_evhoe_2021.csv"), sep = ";", header = T, dec = ".")
# density distribution from biomass value
density_distribution <- trawling_data_evhoe21%>%
select(Nom_Scientifique, Tot_V_HV, Code_Station)%>%
#selection of mesopelagic trawling
filter(Code_Station%in% c("Z0524", "Z0518", "Z0512", "Z0508",
"Z0503", "Z0497", "Z0492"))%>%
#selection of species sampled for isotope
filter(Nom_Scientifique%in%c("Arctozenus risso", "Argyropelecus olfersii", "Benthosema glaciale",
"Cyclothone", "Lampanyctus crocodilus", "Lampanyctus macdonaldi",
"Lestidiops sphyrenoides", "Maulisia argipalla", "Maurolicus muelleri",
"Melanostigma atlanticum", "Myctophum punctatum", "Notoscopelus bolini",
"Notoscopelus kroyeri", "Searsia koefoedi", "Serrivomer beanii",
"Xenodermichthys copei"))%>%
mutate(Nom_Scientifique=recode(Nom_Scientifique, "Cyclothone"="Cyclothone spp."))%>%
mutate(trawling_depth= case_when(Code_Station %in% c("Z0508") ~25,
Code_Station %in% c("Z0492") ~370,
Code_Station%in% c("Z0512") ~555,
Code_Station %in% c("Z0503") ~715,
Code_Station %in% c("Z0518") ~1000,
Code_Station %in% c("Z0524") ~1010,
Code_Station %in% c("Z0497") ~1335))%>%
distinct()%>%
group_by(Nom_Scientifique)%>%
mutate(sum_sp=sum(Tot_V_HV))%>%
ungroup()%>%
group_by(trawling_depth, Nom_Scientifique)%>%
mutate(pourcentage_bio=sum(Tot_V_HV/sum_sp*100))%>%
# Selection of trawling depth
select(Nom_Scientifique, trawling_depth, pourcentage_bio)%>%
# to have a round number to be able to multiply it afterwards
mutate(across(pourcentage_bio, round, 0)) %>%
mutate(n_bio = as.integer(pourcentage_bio))%>%
select(-c(pourcentage_bio))%>%
tidyr::uncount(n_bio)
conclusion: modèle stochastique. Sans pondération de la biomasse toutes les espèces ont le même poids dans l’analyse.